Partners: Wageningen Environmental Research
This use case investigates the impact of agriculture activities on biodiversity within the agricultural landscape as the main environment. The main objective is to improve the knowledge about the correlation between biodiversity and different agricultural practices using a machine learning approach which is consistent across different regions. This would provide a step forward in making more precise estimates of e.g. biodiversity in a spatial context, by linking biodiversity with human activities in agricultural areas and related changes in the physical conditions (e.g., soil, groundwater, emissions etc.). Finally, it aims at increasing awareness about data cubes and AI in domain stakeholders involved in the smart agriculture and biodiversity fields. This is particularly interesting for stakeholders such as local policy makers and environmental organisations, to support them in making better-informed decisions such as selecting more nature-inclusive practices promoting biodiversity.
As the basic conceptual design of the agriculture – biodiversity interaction, this use case uses the Dutch Biodiversity Monitor (DBM)[1], which measures the effect on biodiversity from the impact agricultural activities have on the physical conditions of the environment. One of the major goals of the DBM is to reward farmers for their performance on biodiversity. This can be done by multiple agents such as value chain partners, regional governments and possibly also through payments of the Common Agricultural Policy (CAP).
In the use case, three main data categories are considered: biodiversity data, environmental data, and agricultural data (Figure 2). Each of these data categories is handled primarily within their individual processing flow where distinctive data cubes are generated. The flows are then ultimately merged using causal machine learning.
